Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

Background: Health recommender systems (HRSs) offer the potential to motivate and
engage users to change their behavior by sharing better choices and actionable knowledge …

The remarkable progress in algorithms for machine and deep learning have opened the
doors to new opportunities, and some dark possibilities. However, a bright future awaits …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …

Social media platforms moderate content for each user by incorporating the outputs of both
platform-wide content moderation systems and, in some cases, user-configured personal …

Recommender systems are the algorithms which select, filter, and personalize content
across many of the world's largest platforms and apps. As such, their positive and negative …

Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

Automated recommendations have become a ubiquitous part of today's online user
experience. These systems point us to additional items to purchase in online shops, they …

Recommendation systems, prevalent in many applications, aim to surface to users the right
content at the right time. Recently, researchers have aspired to develop conversational …